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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "x = np.random.randn(50)\n",
    "x = x.reshape(-1,1)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "y = x*2 \n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.plot(x,label='X值')\n",
    "plt.plot(y,label='Y值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lrmodel(input_shape):\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Dense(64,activation='relu',input_shape=input_shape),\n",
    "        tf.keras.layers.Dense(32,activation='relu'),\n",
    "        tf.keras.layers.Dense(16,activation='relu'),\n",
    "        tf.keras.layers.Dense(1)\n",
    "    ])\n",
    "    optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "    model.compile(loss='mse',optimizer=optimizer,metrics=['mae','mse'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = lrmodel(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_2\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_6 (Dense)              (None, 50, 64)            128       \n_________________________________________________________________\ndense_7 (Dense)              (None, 50, 32)            2080      \n_________________________________________________________________\ndense_8 (Dense)              (None, 50, 16)            528       \n_________________________________________________________________\ndense_9 (Dense)              (None, 50, 1)             17        \n=================================================================\nTotal params: 2,753\nTrainable params: 2,753\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "28/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 7.1676e-04 - mae: 0.0210 - mse: 7.1676e-04\n",
      "Epoch 829/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 2.2781e-04 - mae: 0.0116 - mse: 2.2781e-04\n",
      "Epoch 830/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3449e-04 - mae: 0.0096 - mse: 1.3449e-04\n",
      "Epoch 831/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0030 - mae: 0.0386 - mse: 0.0030\n",
      "Epoch 832/1000\n",
      "2/2 [==============================] - 0s 500us/step - loss: 0.0030 - mae: 0.0386 - mse: 0.0030\n",
      "Epoch 833/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 7.0832e-05 - mae: 0.0075 - mse: 7.0832e-05\n",
      "Epoch 834/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 2.2664e-04 - mae: 0.0103 - mse: 2.2664e-04\n",
      "Epoch 835/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 4.4810e-04 - mae: 0.0131 - mse: 4.4810e-04\n",
      "Epoch 836/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 2.8307e-04 - mae: 0.0112 - mse: 2.8307e-04\n",
      "Epoch 837/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0012 - mae: 0.0210 - mse: 0.0012\n",
      "Epoch 838/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 3.4191e-04 - mae: 0.0147 - mse: 3.4191e-04\n",
      "Epoch 839/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 5.1109e-04 - mae: 0.0195 - mse: 5.1109e-04\n",
      "Epoch 840/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0014 - mae: 0.0316 - mse: 0.0014  \n",
      "Epoch 841/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0026 - mae: 0.0446 - mse: 0.0026\n",
      "Epoch 842/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0017 - mae: 0.0321 - mse: 0.0017\n",
      "Epoch 843/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 3.7602e-04 - mae: 0.0155 - mse: 3.7602e-04\n",
      "Epoch 844/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 2.8791e-04 - mae: 0.0122 - mse: 2.8791e-04\n",
      "Epoch 845/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 9.5262e-05 - mae: 0.0080 - mse: 9.5262e-05\n",
      "Epoch 846/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.9781e-04 - mae: 0.0113 - mse: 1.9781e-04\n",
      "Epoch 847/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 7.0394e-04 - mae: 0.0206 - mse: 7.0394e-04\n",
      "Epoch 848/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 6.9730e-04 - mae: 0.0223 - mse: 6.9730e-04\n",
      "Epoch 849/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0017 - mae: 0.0350 - mse: 0.0017\n",
      "Epoch 850/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0023 - mae: 0.0390 - mse: 0.0023\n",
      "Epoch 851/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0017 - mae: 0.0347 - mse: 0.0017\n",
      "Epoch 852/1000\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.0012 - mae: 0.0292 - mse: 0.0012\n",
      "Epoch 853/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 3.7758e-04 - mae: 0.0158 - mse: 3.7758e-04\n",
      "Epoch 854/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 3.2202e-04 - mae: 0.0141 - mse: 3.2202e-04\n",
      "Epoch 855/1000\n",
      "2/2 [==============================] - 0s 499us/step - loss: 8.5218e-05 - mae: 0.0078 - mse: 8.5218e-05\n",
      "Epoch 856/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 2.2721e-04 - mae: 0.0112 - mse: 2.2721e-04\n",
      "Epoch 857/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 4.2985e-04 - mae: 0.0167 - mse: 4.2985e-04\n",
      "Epoch 858/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0020 - mae: 0.0390 - mse: 0.0020\n",
      "Epoch 859/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0033 - mae: 0.0510 - mse: 0.0033\n",
      "Epoch 860/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0016 - mae: 0.0334 - mse: 0.0016\n",
      "Epoch 861/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 2.7654e-04 - mae: 0.0122 - mse: 2.7654e-04\n",
      "Epoch 862/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 4.2215e-04 - mae: 0.0150 - mse: 4.2215e-04\n",
      "Epoch 863/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 6.3957e-05 - mae: 0.0069 - mse: 6.3957e-05\n",
      "Epoch 864/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 6.6637e-05 - mae: 0.0065 - mse: 6.6637e-05\n",
      "Epoch 865/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 3.8000e-04 - mae: 0.0127 - mse: 3.8000e-04\n",
      "Epoch 866/1000\n",
      "2/2 [==============================] - 0s 998us/step - loss: 0.0013 - mae: 0.0239 - mse: 0.0013\n",
      "Epoch 867/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0010 - mae: 0.0200 - mse: 0.0010\n",
      "Epoch 868/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 2.9690e-04 - mae: 0.0117 - mse: 2.9690e-04\n",
      "Epoch 869/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0015 - mae: 0.0282 - mse: 0.0015\n",
      "Epoch 870/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 8.9805e-05 - mae: 0.0072 - mse: 8.9805e-05\n",
      "Epoch 871/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 5.9175e-04 - mae: 0.0186 - mse: 5.9175e-04\n",
      "Epoch 872/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0030 - mae: 0.0460 - mse: 0.0030\n",
      "Epoch 873/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.8211e-04 - mae: 0.0106 - mse: 1.8211e-04\n",
      "Epoch 874/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 1.7015e-04 - mae: 0.0100 - mse: 1.7015e-04\n",
      "Epoch 875/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0011 - mae: 0.0231 - mse: 0.0011\n",
      "Epoch 876/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 5.9915e-04 - mae: 0.0188 - mse: 5.9915e-04\n",
      "Epoch 877/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0014 - mae: 0.0320 - mse: 0.0014\n",
      "Epoch 878/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0011 - mae: 0.0283 - mse: 0.0011\n",
      "Epoch 879/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0017 - mae: 0.0362 - mse: 0.0017\n",
      "Epoch 880/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 3.9231e-04 - mae: 0.0152 - mse: 3.9231e-04\n",
      "Epoch 881/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0016 - mae: 0.0251 - mse: 0.0016\n",
      "Epoch 882/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.6522e-04 - mae: 0.0096 - mse: 1.6522e-04\n",
      "Epoch 883/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 8.2325e-05 - mae: 0.0076 - mse: 8.2325e-05\n",
      "Epoch 884/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 4.7497e-04 - mae: 0.0191 - mse: 4.7497e-04\n",
      "Epoch 885/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0028 - mae: 0.0417 - mse: 0.0028\n",
      "Epoch 886/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0017 - mae: 0.0363 - mse: 0.0017\n",
      "Epoch 887/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 5.3689e-04 - mae: 0.0194 - mse: 5.3689e-04\n",
      "Epoch 888/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 4.3621e-04 - mae: 0.0154 - mse: 4.3621e-04\n",
      "Epoch 889/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 3.9120e-04 - mae: 0.0147 - mse: 3.9120e-04\n",
      "Epoch 890/1000\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 3.2927e-05 - mae: 0.0047 - mse: 3.2927e-05\n",
      "Epoch 891/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 2.4567e-04 - mae: 0.0122 - mse: 2.4567e-04\n",
      "Epoch 892/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 8.8347e-04 - mae: 0.0259 - mse: 8.8347e-04\n",
      "Epoch 893/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0027 - mae: 0.0404 - mse: 0.0027\n",
      "Epoch 894/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0026 - mae: 0.0450 - mse: 0.0026\n",
      "Epoch 895/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0012 - mae: 0.0251 - mse: 0.0012\n",
      "Epoch 896/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 6.6083e-04 - mae: 0.0185 - mse: 6.6083e-04\n",
      "Epoch 897/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 7.6515e-04 - mae: 0.0207 - mse: 7.6515e-04\n",
      "Epoch 898/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 4.7219e-05 - mae: 0.0061 - mse: 4.7219e-05\n",
      "Epoch 899/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.0985e-04 - mae: 0.0092 - mse: 1.0985e-04\n",
      "Epoch 900/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 2.5793e-04 - mae: 0.0144 - mse: 2.5793e-04\n",
      "Epoch 901/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0021 - mae: 0.0408 - mse: 0.0021\n",
      "Epoch 902/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0017 - mae: 0.0341 - mse: 0.0017\n",
      "Epoch 903/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 6.0451e-04 - mae: 0.0202 - mse: 6.0451e-04\n",
      "Epoch 904/1000\n",
      "2/2 [==============================] - 0s 500us/step - loss: 1.2503e-04 - mae: 0.0099 - mse: 1.2503e-04\n",
      "Epoch 905/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 2.0880e-04 - mae: 0.0124 - mse: 2.0880e-04\n",
      "Epoch 906/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0017 - mae: 0.0327 - mse: 0.0017\n",
      "Epoch 907/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0026 - mae: 0.0369 - mse: 0.0026\n",
      "Epoch 908/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0010 - mae: 0.0217 - mse: 0.0010\n",
      "Epoch 909/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 3.5274e-05 - mae: 0.0045 - mse: 3.5274e-05\n",
      "Epoch 910/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 2.9501e-05 - mae: 0.0047 - mse: 2.9501e-05\n",
      "Epoch 911/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 7.9966e-05 - mae: 0.0075 - mse: 7.9966e-05\n",
      "Epoch 912/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0014 - mae: 0.0268 - mse: 0.0014  \n",
      "Epoch 913/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0014 - mae: 0.0327 - mse: 0.0014\n",
      "Epoch 914/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0012 - mae: 0.0303 - mse: 0.0012 \n",
      "Epoch 915/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0028 - mae: 0.0414 - mse: 0.0028\n",
      "Epoch 916/1000\n",
      "2/2 [==============================] - 0s 499us/step - loss: 4.4564e-04 - mae: 0.0171 - mse: 4.4564e-04\n",
      "Epoch 917/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 2.1284e-04 - mae: 0.0124 - mse: 2.1284e-04\n",
      "Epoch 918/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 3.1629e-04 - mae: 0.0150 - mse: 3.1629e-04\n",
      "Epoch 919/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 8.4098e-04 - mae: 0.0239 - mse: 8.4098e-04\n",
      "Epoch 920/1000\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.0019 - mae: 0.0385 - mse: 0.0019\n",
      "Epoch 921/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0014 - mae: 0.0345 - mse: 0.0014\n",
      "Epoch 922/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 2.0244e-04 - mae: 0.0128 - mse: 2.0244e-04\n",
      "Epoch 923/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.9868e-04 - mae: 0.0126 - mse: 1.9868e-04\n",
      "Epoch 924/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 9.2243e-04 - mae: 0.0216 - mse: 9.2243e-04\n",
      "Epoch 925/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0019 - mae: 0.0319 - mse: 0.0019\n",
      "Epoch 926/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 5.7674e-04 - mae: 0.0212 - mse: 5.7674e-04\n",
      "Epoch 927/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 4.7602e-04 - mae: 0.0164 - mse: 4.7602e-04\n",
      "Epoch 928/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0012 - mae: 0.0220 - mse: 0.0012\n",
      "Epoch 929/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 7.4294e-04 - mae: 0.0169 - mse: 7.4294e-04\n",
      "Epoch 930/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 2.6910e-04 - mae: 0.0118 - mse: 2.6910e-04\n",
      "Epoch 931/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 9.1123e-04 - mae: 0.0256 - mse: 9.1123e-04\n",
      "Epoch 932/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0022 - mae: 0.0372 - mse: 0.0022\n",
      "Epoch 933/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0012 - mae: 0.0299 - mse: 0.0012\n",
      "Epoch 934/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0014 - mae: 0.0339 - mse: 0.0014\n",
      "Epoch 935/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0017 - mae: 0.0332 - mse: 0.0017\n",
      "Epoch 936/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.1441e-04 - mae: 0.0086 - mse: 1.1441e-04\n",
      "Epoch 937/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 4.7389e-05 - mae: 0.0053 - mse: 4.7389e-05\n",
      "Epoch 938/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 6.1806e-05 - mae: 0.0057 - mse: 6.1806e-05\n",
      "Epoch 939/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 7.6305e-05 - mae: 0.0062 - mse: 7.6305e-05\n",
      "Epoch 940/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 5.8566e-04 - mae: 0.0172 - mse: 5.8566e-04\n",
      "Epoch 941/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0012 - mae: 0.0292 - mse: 0.0012    \n",
      "Epoch 942/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0032 - mae: 0.0436 - mse: 0.0032\n",
      "Epoch 943/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0027 - mae: 0.0425 - mse: 0.0027\n",
      "Epoch 944/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 7.1704e-04 - mae: 0.0178 - mse: 7.1704e-04\n",
      "Epoch 945/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 3.6200e-05 - mae: 0.0048 - mse: 3.6200e-05\n",
      "Epoch 946/1000\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.2627e-04 - mae: 0.0072 - mse: 1.2627e-04\n",
      "Epoch 947/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.9496e-04 - mae: 0.0084 - mse: 1.9496e-04\n",
      "Epoch 948/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 6.2630e-04 - mae: 0.0155 - mse: 6.2630e-04\n",
      "Epoch 949/1000\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.0011 - mae: 0.0248 - mse: 0.0011\n",
      "Epoch 950/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 6.3818e-04 - mae: 0.0211 - mse: 6.3818e-04\n",
      "Epoch 951/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0020 - mae: 0.0349 - mse: 0.0020\n",
      "Epoch 952/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0023 - mae: 0.0376 - mse: 0.0023\n",
      "Epoch 953/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 6.1938e-04 - mae: 0.0197 - mse: 6.1938e-04\n",
      "Epoch 954/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 4.2249e-04 - mae: 0.0160 - mse: 4.2249e-04\n",
      "Epoch 955/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0011 - mae: 0.0222 - mse: 0.0011\n",
      "Epoch 956/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 9.7516e-04 - mae: 0.0215 - mse: 9.7516e-04\n",
      "Epoch 957/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 7.2956e-05 - mae: 0.0067 - mse: 7.2956e-05\n",
      "Epoch 958/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 3.1060e-04 - mae: 0.0126 - mse: 3.1060e-04\n",
      "Epoch 959/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0017 - mae: 0.0311 - mse: 0.0017\n",
      "Epoch 960/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0024 - mae: 0.0373 - mse: 0.0024\n",
      "Epoch 961/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 8.3813e-04 - mae: 0.0244 - mse: 8.3813e-04\n",
      "Epoch 962/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 3.6727e-04 - mae: 0.0166 - mse: 3.6727e-04\n",
      "Epoch 963/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0013 - mae: 0.0292 - mse: 0.0013\n",
      "Epoch 964/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0012 - mae: 0.0264 - mse: 0.0012\n",
      "Epoch 965/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 2.9370e-04 - mae: 0.0142 - mse: 2.9370e-04\n",
      "Epoch 966/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0011 - mae: 0.0234 - mse: 0.0011 \n",
      "Epoch 967/1000\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.0012 - mae: 0.0243 - mse: 0.0012\n",
      "Epoch 968/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0011 - mae: 0.0258 - mse: 0.0011\n",
      "Epoch 969/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0011 - mae: 0.0269 - mse: 0.0011\n",
      "Epoch 970/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 8.1500e-04 - mae: 0.0257 - mse: 8.1500e-04\n",
      "Epoch 971/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0014 - mae: 0.0302 - mse: 0.0014\n",
      "Epoch 972/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 8.2379e-04 - mae: 0.0249 - mse: 8.2379e-04\n",
      "Epoch 973/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 6.4403e-04 - mae: 0.0227 - mse: 6.4403e-04\n",
      "Epoch 974/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 2.3038e-04 - mae: 0.0129 - mse: 2.3038e-04\n",
      "Epoch 975/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 5.0222e-04 - mae: 0.0181 - mse: 5.0222e-04\n",
      "Epoch 976/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 5.8167e-04 - mae: 0.0207 - mse: 5.8167e-04\n",
      "Epoch 977/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0029 - mae: 0.0465 - mse: 0.0029\n",
      "Epoch 978/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0017 - mae: 0.0373 - mse: 0.0017\n",
      "Epoch 979/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 8.1712e-04 - mae: 0.0225 - mse: 8.1712e-04\n",
      "Epoch 980/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 5.4869e-04 - mae: 0.0166 - mse: 5.4869e-04\n",
      "Epoch 981/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 1.2664e-04 - mae: 0.0095 - mse: 1.2664e-04\n",
      "Epoch 982/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 1.1035e-04 - mae: 0.0093 - mse: 1.1035e-04\n",
      "Epoch 983/1000\n",
      "2/2 [==============================] - 0s 961us/step - loss: 2.0232e-04 - mae: 0.0110 - mse: 2.0232e-04\n",
      "Epoch 984/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 4.7552e-04 - mae: 0.0186 - mse: 4.7552e-04\n",
      "Epoch 985/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0023 - mae: 0.0423 - mse: 0.0023\n",
      "Epoch 986/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 0.0028 - mae: 0.0415 - mse: 0.0028\n",
      "Epoch 987/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 2.5565e-04 - mae: 0.0132 - mse: 2.5565e-04\n",
      "Epoch 988/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 2.6930e-04 - mae: 0.0116 - mse: 2.6930e-04\n",
      "Epoch 989/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 1.7421e-04 - mae: 0.0115 - mse: 1.7421e-04\n",
      "Epoch 990/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 3.0270e-04 - mae: 0.0138 - mse: 3.0270e-04\n",
      "Epoch 991/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 9.4260e-04 - mae: 0.0245 - mse: 9.4260e-04\n",
      "Epoch 992/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 4.3437e-04 - mae: 0.0192 - mse: 4.3437e-04\n",
      "Epoch 993/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0014 - mae: 0.0315 - mse: 0.0014    \n",
      "Epoch 994/1000\n",
      "2/2 [==============================] - 0s 999us/step - loss: 0.0020 - mae: 0.0391 - mse: 0.0020\n",
      "Epoch 995/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.0040 - mae: 0.0530 - mse: 0.0040\n",
      "Epoch 996/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 4.7010e-05 - mae: 0.0062 - mse: 4.7010e-05\n",
      "Epoch 997/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 2.4502e-05 - mae: 0.0045 - mse: 2.4502e-05\n",
      "Epoch 998/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 3.9547e-05 - mae: 0.0047 - mse: 3.9547e-05\n",
      "Epoch 999/1000\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.0226e-04 - mae: 0.0073 - mse: 1.0226e-04\n",
      "Epoch 1000/1000\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 5.5910e-04 - mae: 0.0167 - mse: 5.5910e-04\n"
     ]
    }
   ],
   "source": [
    "epoches = 1000\n",
    "\n",
    "history = model.fit(x,y,epochs=epoches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "newx =np.array([1,2,3,4,5]).reshape(-1,1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 50, 1) for input Tensor(\"dense_6_input:0\", shape=(None, 50, 1), dtype=float32), but it was called on an input with incompatible shape (None, 1, 1).\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[[1.9884784]],\n",
       "\n",
       "       [[3.9728644]],\n",
       "\n",
       "       [[5.9559045]],\n",
       "\n",
       "       [[7.938585 ]],\n",
       "\n",
       "       [[9.921557 ]]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "predict = model.predict(newx)\n",
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}